IoT based cloud network for smart health care using optimization algorithm

In recent years, the Internet of Things (IoT) technology has drawn significant interest as it can decrease the liability on healthcare services on account of an expansion in notable illnesses and the population's growing age. The best decision of virtualized resources in cloud computing plays a...

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Autores principales:	Ankur Goyal, Hoshiyar Singh kanyal, Shivkant Kaushik, Rijwan Khan
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	IoT Cloud computing probabilistic neural network Particle swarm optimization Genetic algorithm Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/b1f8b091bde349d49ca9dee8beb6e7fa
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spelling	oai:doaj.org-article:b1f8b091bde349d49ca9dee8beb6e7fa2021-12-02T05:02:12ZIoT based cloud network for smart health care using optimization algorithm2352-914810.1016/j.imu.2021.100792https://doaj.org/article/b1f8b091bde349d49ca9dee8beb6e7fa2021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352914821002628https://doaj.org/toc/2352-9148In recent years, the Internet of Things (IoT) technology has drawn significant interest as it can decrease the liability on healthcare services on account of an expansion in notable illnesses and the population's growing age. The best decision of virtualized resources in cloud computing plays a prominent role in improving the efficiency of cloud computing, reducing the overall time required to complete patient demands by turnaround time, and maximizing CPU use and waiting times. An improved PSO approach for improving physiological sensor-data fusion calculation accuracy in the Internet of Things (IoT) framework has been introduced in this paper. This approach helps in automatically diagnosing natural epilepsy and brain fatalities from detected EEG signals received by the health center. There is also an application of discrete wavelet transform for featuring abolition. Neurological disability diagnosis using particle swarm computation helps in optimizing the propagation of neural networks and EEG. A device that is more effective in terms of result precision requires complicated signals like EEG for input. While the primary ANN model diagnoses signals from the patient, it does not optimize the parameters. It, therefore, implies that the PSO-ANNs have a perfect number of cells in the secret layer to provide better performance than the basic EEG-Signal ANN-Model. The proposed model effectively improved about 4.6% reciprocal to the Genetic Algorithm optimum selection model in execution times. Moreover, the test result determines the sensitivity and accuracy metrics for different neurological disorders of the patient.Ankur GoyalHoshiyar Singh kanyalShivkant KaushikRijwan KhanElsevierarticleIoTCloud computing probabilistic neural networkParticle swarm optimizationGenetic algorithmComputer applications to medicine. Medical informaticsR858-859.7ENInformatics in Medicine Unlocked, Vol 27, Iss , Pp 100792- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	IoT Cloud computing probabilistic neural network Particle swarm optimization Genetic algorithm Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	IoT Cloud computing probabilistic neural network Particle swarm optimization Genetic algorithm Computer applications to medicine. Medical informatics R858-859.7 Ankur Goyal Hoshiyar Singh kanyal Shivkant Kaushik Rijwan Khan IoT based cloud network for smart health care using optimization algorithm
description	In recent years, the Internet of Things (IoT) technology has drawn significant interest as it can decrease the liability on healthcare services on account of an expansion in notable illnesses and the population's growing age. The best decision of virtualized resources in cloud computing plays a prominent role in improving the efficiency of cloud computing, reducing the overall time required to complete patient demands by turnaround time, and maximizing CPU use and waiting times. An improved PSO approach for improving physiological sensor-data fusion calculation accuracy in the Internet of Things (IoT) framework has been introduced in this paper. This approach helps in automatically diagnosing natural epilepsy and brain fatalities from detected EEG signals received by the health center. There is also an application of discrete wavelet transform for featuring abolition. Neurological disability diagnosis using particle swarm computation helps in optimizing the propagation of neural networks and EEG. A device that is more effective in terms of result precision requires complicated signals like EEG for input. While the primary ANN model diagnoses signals from the patient, it does not optimize the parameters. It, therefore, implies that the PSO-ANNs have a perfect number of cells in the secret layer to provide better performance than the basic EEG-Signal ANN-Model. The proposed model effectively improved about 4.6% reciprocal to the Genetic Algorithm optimum selection model in execution times. Moreover, the test result determines the sensitivity and accuracy metrics for different neurological disorders of the patient.
format	article
author	Ankur Goyal Hoshiyar Singh kanyal Shivkant Kaushik Rijwan Khan
author_facet	Ankur Goyal Hoshiyar Singh kanyal Shivkant Kaushik Rijwan Khan
author_sort	Ankur Goyal
title	IoT based cloud network for smart health care using optimization algorithm
title_short	IoT based cloud network for smart health care using optimization algorithm
title_full	IoT based cloud network for smart health care using optimization algorithm
title_fullStr	IoT based cloud network for smart health care using optimization algorithm
title_full_unstemmed	IoT based cloud network for smart health care using optimization algorithm
title_sort	iot based cloud network for smart health care using optimization algorithm
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/b1f8b091bde349d49ca9dee8beb6e7fa
work_keys_str_mv	AT ankurgoyal iotbasedcloudnetworkforsmarthealthcareusingoptimizationalgorithm AT hoshiyarsinghkanyal iotbasedcloudnetworkforsmarthealthcareusingoptimizationalgorithm AT shivkantkaushik iotbasedcloudnetworkforsmarthealthcareusingoptimizationalgorithm AT rijwankhan iotbasedcloudnetworkforsmarthealthcareusingoptimizationalgorithm
_version_	1718400787690815488

IoT based cloud network for smart health care using optimization algorithm

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